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The Kyoto Vision is an international declaration urging governments and research institutions to improve how science is funded and organized, integrate advanced AI into research, and develop scientific talent. Endorsed in Kyoto on October 4, 2026, by the United States and 16 other countries, it sets out shared aspirations—not a common budget, delivery schedule, or technical definition of “Super Intelligence.”
What is the Kyoto Vision?
The Kyoto Vision for a Golden Age of Science is a one-page declaration endorsed alongside the 23rd annual Science and Technology in Society (STS) Forum meeting in Kyoto, Japan. The declaration presents science and technology as contributors to healthier, more prosperous societies and argues that new research methods and “Super Intelligence” (SI) are opening new frontiers.
Its central aim is to renew science ecosystems and strengthen their ability to produce knowledge. The endorsers say that research productivity should increase, unnecessary administrative burdens should fall, and funding systems should make room for ambitious, transformative ideas. The White House announced that 17 countries endorsed the declaration: the United States and 16 other nations.
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The declaration groups its agenda into three connected directions. AI is only one part: the vision also addresses institutions, funding, research integrity, and the people and technical skills needed to do scientific work.
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Improve the institutions of discovery
The Vision favors a varied mix of ways to support research, including long-duration awards, rapid grants, prizes, and challenges. It encourages experimentation with institutional models that give researchers different combinations of autonomy, resources, and direction, rather than prescribing one universal structure.
It also welcomes metascience: the use of empirical methods to improve how science is conducted, organized, and funded. The declaration links institutional improvement to public trust, which it says depends on scientific rigor and integrity.
Integrate SI into research workflows
The declaration describes SI as accelerating some existing research tasks and enabling new forms of inquiry. It encourages thoughtful integration of these systems to help researchers:
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- Reason across large bodies of knowledge.
- Build higher-fidelity models of physical and biological systems.
- Drive autonomous, closed-loop discovery, where research systems can connect computational steps with experiments and use results to guide subsequent work.
It also calls for broader access to SI tools, scientific data, computing infrastructure, and experimental facilities “at the scale researchers now require.” That is a direction, not a specified access program or allocation formula.
Cultivate scientific and technical talent
The Vision calls for early investment in high-potential students and early-career researchers, giving them support, freedom, and opportunity to pursue ambitious questions. It also recognizes that advanced science depends on people who can operate, maintain, and improve sophisticated instruments—not only on researchers designing studies.
Among the approaches it encourages are hands-on training, joint doctoral programs, fellowships, and collaboration across scientific environments.
What does “Super Intelligence” mean here?
“Super Intelligence” is the declaration’s own term, but the document does not define it formally or set a threshold for when a system qualifies. It does not establish that a general-purpose, human-level AI scientist already exists. Instead, it points to research functions: synthesizing knowledge, improving models of physical and biological systems, and supporting autonomous, closed-loop discovery.
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That distinction matters. The Vision communicates a policy ambition to use advanced AI in scientific work; it is not a technical specification, a claim that every listed capability is available now, or a plan to deploy one particular system.
What does the declaration commit countries to do?
The text is an international statement of aims and encouragements, not an operational implementation plan. It contains no shared budget, numerical targets, infrastructure-allocation scheme, or common delivery date. The White House announcement records the endorsement and summarizes the agenda; it does not establish that each signatory has already put every aim into practice.
As a result, the declaration does not let readers compare country-by-country implementation plans or measure progress against a common timetable. Specific national policies, funding decisions, and outcomes would need to be assessed separately.
How does the Vision address scientific integrity?
The declaration treats trust as a condition for scientific progress, not an afterthought to faster or more automated research. It identifies reproducibility, transparency, clear communication of error and uncertainty, unbiased peer review, and recognition of negative and null results as parts of rigorous science.
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Those principles are especially relevant when AI systems contribute to research, because outputs need to be scrutinized and results need to remain interpretable enough for scientific evaluation. RIKEN AGIS, a separate research initiative rather than a signatory or author of the Kyoto Vision, discusses how AI opacity can create tension with reproducibility, transparency, and accountability. Its materials point to careful experiments and systematic recording of conditions as ways to address that tension.
Data readiness is another practical constraint, though it varies by field. RIKEN AGIS describes polymer research as involving costly, multistage experiments and fragmented datasets that are not sufficiently suited to AI. That example illustrates why access to computing alone may not make a research area ready for AI-driven discovery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What related work is happening in Kyoto?
Kyoto University’s Institute of Generative Science preparatory office describes a local interdisciplinary initiative connecting physics, mathematics, information science, philosophy, literature, and law to explore how scientific knowledge and principles emerge alongside generative AI. Its site identifies itself as a temporary preparatory-office website. This initiative provides local context; it is not presented as the body behind the Kyoto Vision or as a signatory to the declaration.
How to judge what happens next
The declaration is best read as a framework of priorities. To assess whether later programs follow through, look for concrete evidence in five areas:
- Research workflows: Are AI systems assisting with analysis, or are they also connected to experiments in closed loops?
- Access: Are tools, suitable data, computing, and experimental facilities available to researchers at meaningful scale?
- Integrity: Do policies preserve reproducibility and transparency, communicate uncertainty, support unbiased review, and value null or negative findings?
- Institutional design and funding: Are there funding mechanisms such as long-term awards, rapid grants, prizes, or challenges, and do institutions test different levels of researcher autonomy?
- Talent and technical capacity: Are early-career researchers supported, and are skilled instrument operators and maintainers trained alongside them?
These questions follow the Vision’s stated priorities, but the declaration itself does not provide country-level plans or common metrics for answering them.
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